[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117095-en":3,"doc-seo-117095-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117095,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Detecting genuine multipartite entanglement via machine learning","This study investigates supervised and semi-supervised machine learning methods for certifying genuine multipartite entanglement (GME) in three-qubit quantum states. Random three-qubit density matrices are generated and an SVM is trained to identify GME. The training procedure for S4VM is further improved by optimizing how prediction samples are grouped and applying iterative predictions. Numerical simulations verify that the proposed S4VM strategy substantially increases prediction accuracy relative to direct grouping.","Detecting genuine multipartite entanglement via machine learning  \narXiv :2311 . 17548v1 [ quant-ph] 29 Nov 2023  \nYi-Jun Luo, 1 Jin-Ming Liu,2 and Chengjie Zhang 1, 2, ∗  \n1 School of Physical Science and Technology, Ningbo University, Ningbo 315211, China  \n2 State Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China  \n(Dated: November 30, 2023)  \nIn recent years, supervised and semi-supervised machine learning methods such as neural networks, support vector machines (SVM), and semi-supervised support vector machines (S4VM) have been widely used in quantum entanglement and quantum steering verification problems. However, few studies have focused on detecting genuine multipartite entanglement based on machine learning. Here, we investigate supervised and semi-supervised machine learning for detecting genuine multipartite entanglement of three-qubit states. We randomly generate three-qubit density matrices, and train an SVM for the detection of genuine multipartite entangled states. Moreover, we improve the training method of S4VM, which optimizes the grouping of prediction samples and then performs iterative predictions. Through numerical simulation, it is confirmed that this method can significantly improve the prediction accuracy.  \nI. INTRODUCTION  \nGenuine multipartite entanglement (GME) is a relevant resource in quantum information processing [1–6] . It is used in many quantum information tasks, such as cluster states in the one-way quantum computing model [7], Greenberger-Horne-Zeilinger (GHZ) and Dicke states in quantum metrology [8, 9], or graph states in quantum error correction codes [10, 11] . Consequently, the GME certification is a central task in the field of quantum information.  \nThere are many entanglement criteria and entanglement measures for bipartite quantum states [1–6], such asthe negativity and its extensions [12–16], the concurrence [17–22], the G-concurrence, [23–26] and the geometric measure of entanglement [26–28], etc. However, for multipartite quantum systems the situation becomes more complicated, as several different entanglement classes exist [29–32] . Among all the multipartite entanglement classes, GME can be viewed as the strongest multipartite entanglement type. A multipartite quantum state contains GME if and only if it cannot be expressed as a convex combination of biseparable states with respect to any bipartitions. Many detection criteria and entanglement measures have been proposed for GME [33–42] .  \nMachine learning is an interdisciplinary field that combines probability theory, statistics, computer science and other domains to study how computers can simulate human learning behavior by constantly reorganizing their existing knowledge structures. According to the learning style, it is mainly divided into four main categories: supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. These methods have been widely used in quantum information, particularly in quantum entanglement classification [43–47], quantum steering [48, 49], quantum nonlocality  \n∗ [chengjie.zhang@gmail.com](chengjie.zhang@gmail.com)  \n[50, 51], spin system [52, 53] and other aspects.  \nSemi-supervised learning classification algorithms area form of semi-supervised learning, including the semisupervised random forest algorithm [54], safe semisupervised support vector machine (S4VM) [55], semisupervised k-nearest neighbors algorithm [56] and other algorithms. Among them, S4VM has obvious effects on anomaly detection [57, 58] and image text classification [59–61], and has also been applied to quantum steering classification problems [49] . By using semi-definite programming (SDP) [62–64] to generate quantum entangled states randomly, semi-supervised algorithms can be applied to predict a large number of unlabeled quantum states from a small number of labeled states.  \nRecently, supervised and s","cbCaidHSgPhrcOQl","https://ap.wps.com/l/cbCaidHSgPhrcOQl","pdf",1083146,1,9,"English","en",105,"# Introduction\n## Genuine multipartite entanglement in quantum information\n## Role of machine learning in entanglement detection\n# Supervised machine learning\n## Methods and state representation\n## Entanglement witness framework\n# Semi-supervised machine learning\n## Group selection and iterative prediction\n# Conclusion","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper targets detecting genuine multipartite entanglement (GME) of three-qubit states using supervised and semi-supervised machine learning.\"},{\"question\":\"How is the SVM used for GME detection?\",\"answer\":\"Three-qubit density matrices are randomly generated, and an SVM is trained to detect states that are genuinely multipartite entangled.\"},{\"question\":\"What improvement is proposed for S4VM?\",\"answer\":\"The paper proposes an improved training method that optimizes grouping of prediction samples and then performs iterative predictions to raise classification accuracy.\"}]","Detecting genuine multipartite entanglement via machine learning | 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problem does the paper address?","Question",{"text":75,"@type":76},"The paper targets detecting genuine multipartite entanglement (GME) of three-qubit states using supervised and semi-supervised machine learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the SVM used for GME detection?",{"text":80,"@type":76},"Three-qubit density matrices are randomly generated, and an SVM is trained to detect states that are genuinely multipartite entangled.",{"name":82,"@type":73,"acceptedAnswer":83},"What improvement is proposed for S4VM?",{"text":84,"@type":76},"The paper proposes an improved training method that optimizes grouping of prediction samples and then performs iterative predictions to raise classification 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